Vehicles may be equipped with many sensors that facilitate perceiving other vehicles, obstacles, and pedestrians in a surrounding environment. Perception and reasoning by the systems of such vehicles facilitates the vehicles (e.g., autonomous or semi-autonomous) in making decisions according to the perceived information. However, making decisions in this way is generally only effective if sensors work reliably, which is not always a guarantee for the noted systems when deployed in the real-world amongst other vehicles.
In the event of sensor failure, the vehicle systems can rely on past sensor readings for decision making. For example, autonomous vehicles generally plan and execute maneuvers while safely avoiding other obstacles on the road. To do so, the vehicle systems use knowledge of other vehicles surrounding the autonomous vehicle and information about other aspects of the environment (e.g., lane markers, obstacles, etc.) to plan into the near future. However, lack of data from the failed sensors can cause difficulties in performing the planning.